# Copyright 2019-2020 The Lux Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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# See the License for the specific language governing permissions and
# limitations under the License.
import lux
from lux.vis.Vis import Vis
from lux.processor.Compiler import Compiler
from lux.utils import utils
from lux.interestingness.interestingness import interestingness
[docs]def generalize(ldf):
"""
Generates all possible visualizations when one attribute or filter from the current vis is removed.
Parameters
----------
ldf : lux.core.frame
LuxDataFrame with underspecified intent.
Returns
-------
recommendations : Dict[str,obj]
object with a collection of visualizations that result from the Generalize action.
"""
# takes in a dataObject and generates a list of new dataObjects, each with a single measure from the original object removed
# --> return list of dataObjects with corresponding interestingness scores
output = []
excluded_columns = []
attributes = list(filter(lambda x: x.value == "" and x.attribute != "Record", ldf._intent))
filters = utils.get_filter_specs(ldf._intent)
fltr_str = [fltr.attribute + fltr.filter_op + str(fltr.value) for fltr in filters]
attr_str = [str(clause.attribute) for clause in attributes]
intended_attrs = f'<p class="highlight-intent">{", ".join(attr_str + fltr_str)}</p>'
recommendation = {
"action": "Generalize",
"description": f"Remove an attribute or filter from {intended_attrs}.",
"long_description": f"Remove one aspect of the Current Vis. We can either remove an attribute or filter from {intended_attrs}.",
}
# to observe a more general trend
# if we do no have enough column attributes or too many, return no vis.
if len(attributes) < 1 or len(attributes) > 4:
recommendation["collection"] = []
return recommendation
# for each column specification, create a copy of the ldf's vis and remove the column specification
# then append the vis to the output
if len(attributes) > 1:
for clause in attributes:
columns = clause.attribute
if type(columns) == list:
for column in columns:
if column not in excluded_columns:
temp_vis = Vis(ldf.copy_intent(), score=1)
temp_vis.remove_column_from_spec(column, remove_first=True)
excluded_columns.append(column)
output.append(temp_vis)
else:
if columns not in excluded_columns:
temp_vis = Vis(ldf.copy_intent(), score=1)
temp_vis.remove_column_from_spec(columns, remove_first=True)
excluded_columns.append(columns)
output.append(temp_vis)
# for each filter specification, create a copy of the ldf's current vis and remove the filter specification,
# then append the vis to the output
for clause in filters:
# new_spec = ldf._intent.copy()
# new_spec.remove_column_from_spec(new_spec.attribute)
temp_vis = Vis(
ldf.current_vis[0]._inferred_intent.copy(),
source=ldf,
title="Overall",
score=0,
)
temp_vis.remove_filter_from_spec(clause.value)
output.append(temp_vis)
vlist = lux.vis.VisList.VisList(output, source=ldf)
# Ignore interestingness sorting since Generalize yields very few vis (preserve order of remove attribute, then remove filters)
# for vis in vlist:
# vis.score = interestingness(vis,ldf)
vlist.remove_duplicates()
vlist.sort(remove_invalid=True)
vlist._collection = list(filter(lambda x: x.score != -1, vlist._collection))
recommendation["collection"] = vlist
return recommendation